A New Method for Diagnosing Patients Suspected of Bone Marrow Metastasis in the Presence of Outliers

Document Type : Original Manuscript


1 Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 Industrial Engineering Department, Shahed University, Tehran, Iran

3 Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran

4 Department of Electrical and Electronic Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran


In recent years, medical images have played an essential role in diagnosis, treatment, and training areas. Thus, any advancement in this field can help doctors in diagnosing. On the other hand, statistical process control (SPC) is now widely used in monitoring healthcare processes. In this research, using the image processing techniques and feature extraction methods (two-dimensional discrete wavelet), we propose some multivariate control charts to diagnose the type of bone marrow of the patients suspected of bone marrow metastasis in the pelvic region with early breast tumors. For this, 76 features (energy and histogram of oriented gradient) are extracted from the image. Next, using the GA, six features are selected and constitute a feature vector. Based on the feature vector, Hotelling’s T2 multivariate control charts are developed. Moreover, considering the high sensitivity of the classic estimators to outliers and contaminated data, we provide a robust Hotelling’s T2 control chart. Finally, we compare the ARL performance of the robust and the classic Hotelling’s T2 control charts in Phase II in the presence of local outliers in the Phase I data. The results confirmed the superiority of the robust version.

Graphical Abstract

A New Method for Diagnosing Patients Suspected of Bone Marrow Metastasis in the Presence of Outliers


  • Feature extraction from medical images using a two-dimensional discrete transformation.
  • A new method for diagnosing the type of bone marrows using multivariate control charts.
  • Proposing and comparing two multivariate control charts (classic and robust) for bone marrow diagnosis.
  • Simulating the diagnosis process in the presence of data contamination.
  • The robust multivariate control chart outperforms the classic one.


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